Package: simtrial 0.4.2

Yujie Zhao

simtrial: Clinical Trial Simulation

Provides some basic routines for simulating a clinical trial. The primary intent is to provide some tools to generate trial simulations for trials with time to event outcomes. Piecewise exponential failure rates and piecewise constant enrollment rates are the underlying mechanism used to simulate a broad range of scenarios such as those presented in Lin et al. (2020) <doi:10.1080/19466315.2019.1697738>. However, the basic generation of data is done using pipes to allow maximum flexibility for users to meet different needs.

Authors:Keaven Anderson [aut], Yujie Zhao [ctb, cre], John Blischak [ctb], Nan Xiao [ctb], Yilong Zhang [aut], Jianxiao Yang [ctb], Lili Ling [ctb], Xintong Li [ctb], Ruixue Wang [ctb], Yi Cui [ctb], Ping Yang [ctb], Yalin Zhu [ctb], Heng Zhou [ctb], Amin Shirazi [ctb], Cole Manschot [ctb], Merck & Co., Inc., Rahway, NJ, USA and its affiliates [cph]

simtrial_0.4.2.tar.gz
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simtrial_0.4.2.tgz(r-4.4-x86_64)simtrial_0.4.2.tgz(r-4.4-arm64)simtrial_0.4.2.tgz(r-4.3-x86_64)simtrial_0.4.2.tgz(r-4.3-arm64)
simtrial_0.4.2.tar.gz(r-4.5-noble)simtrial_0.4.2.tar.gz(r-4.4-noble)
simtrial_0.4.2.tgz(r-4.4-emscripten)simtrial_0.4.2.tgz(r-4.3-emscripten)
simtrial.pdf |simtrial.html✨
simtrial/json (API)
NEWS

# Install 'simtrial' in R:
install.packages('simtrial', repos = c('https://merck.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/merck/simtrial/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • ex1_delayed_effect - Time-to-event data example 1 for non-proportional hazards working group
  • ex2_delayed_effect - Time-to-event data example 2 for non-proportional hazards working group
  • ex3_cure_with_ph - Time-to-event data example 3 for non-proportional hazards working group
  • ex4_belly - Time-to-event data example 4 for non-proportional hazards working group
  • ex5_widening - Time-to-event data example 5 for non-proportional hazards working group
  • ex6_crossing - Time-to-event data example 6 for non-proportional hazards working group
  • mb_delayed_effect - Simulated survival dataset with delayed treatment effect

On CRAN:

9.10 score 18 stars 54 scripts 695 downloads 24 exports 88 dependencies

Last updated 4 days agofrom:d2f4127c30. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 18 2024
R-4.5-win-x86_64NOTENov 18 2024
R-4.5-linux-x86_64NOTENov 18 2024
R-4.4-win-x86_64NOTENov 18 2024
R-4.4-mac-x86_64NOTENov 18 2024
R-4.4-mac-aarch64NOTENov 18 2024
R-4.3-win-x86_64NOTENov 18 2024
R-4.3-mac-x86_64NOTENov 18 2024
R-4.3-mac-aarch64NOTENov 18 2024

Exports:as_gtcounting_processcreate_cutcreate_testcut_data_by_datecut_data_by_eventearly_zerofhfit_pwexpget_analysis_dateget_cut_date_by_eventmaxcombombmilestonemultitestrandomize_by_fixed_blockrmstrpwexprpwexp_enrollsim_fixed_nsim_gs_nsim_pw_survto_sim_pw_survwlr

Dependencies:base64encbigDbitopsbslibcachemclicodetoolscolorspacecommonmarkcorpcorcpp11curldata.tabledigestdoFuturedplyrevaluatefansifarverfastmapfontawesomeforeachfsfuturefuture.applygenericsggplot2globalsgluegsDesigngsDesign2gtgtablehighrhtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonlitejuicyjuiceknitrlabelinglatticelifecyclelistenvmagrittrmarkdownMASSMatrixmemoisemgcvmimemunsellmvtnormnlmenpsurvSSparallellypillarpkgconfigpurrrr2rtfR6rappdirsRColorBrewerRcppreactablereactRrlangrmarkdownsassscalesstringistringrsurvivaltibbletidyrtidyselecttinytexutf8V8vctrsviridisLitewithrxfunxml2xtableyaml

Approximating an arbitrary hazard function

Keaven Anderson

Rendered fromarbitrary-hazard.Rmdusingknitr::rmarkdownon Nov 18 2024.

Last update: 2024-05-07
Started: 2023-11-28

Basic tools for time-to-event trial simulation and testing

Keaven Anderson

Rendered fromroutines.Rmdusingknitr::rmarkdownon Nov 18 2024.

Last update: 2024-05-07
Started: 2023-11-28

Computing p-values for Fleming-Harrington weighted logrank tests and the MaxCombo test

Keaven Anderson, Yujie Zhao

Rendered frommaxcombo.Rmdusingknitr::rmarkdownon Nov 18 2024.

Last update: 2024-05-07
Started: 2024-04-11

Restricted mean survival time (RMST)

Yujie Zhao, Yilong Zhang

Rendered fromrmst.Rmdusingknitr::rmarkdownon Nov 18 2024.

Last update: 2024-07-23
Started: 2024-02-05

Simulating time-to-event trials in parallel

Cole Manschot, Nan Xiao

Rendered fromparallel.Rmdusingknitr::rmarkdownon Nov 18 2024.

Last update: 2024-08-08
Started: 2023-09-25

TTE simulation data manipulations

Keaven Anderson

Rendered fromworkflow.Rmdusingknitr::rmarkdownon Nov 18 2024.

Last update: 2024-08-08
Started: 2023-05-30

Using the Magirr-Burman weights for testing

Keaven Anderson, Yujie Zhao

Rendered frommodest-wlrt.Rmdusingknitr::rmarkdownon Nov 18 2024.

Last update: 2024-08-08
Started: 2023-11-28

Readme and manuals

Help Manual

Help pageTopics
Convert summary table to a gt objectas_gt as_gt.simtrial_gs_wlr
Process survival data into counting process formatcounting_process
Create a cutting functioncreate_cut
Create a cutting test functioncreate_test
Cut a dataset for analysis at a specified datecut_data_by_date
Cut a dataset for analysis at a specified event countcut_data_by_event
Zero early weighting functionearly_zero
Time-to-event data example 1 for non-proportional hazards working groupex1_delayed_effect
Time-to-event data example 2 for non-proportional hazards working groupex2_delayed_effect
Time-to-event data example 3 for non-proportional hazards working groupex3_cure_with_ph
Time-to-event data example 4 for non-proportional hazards working groupex4_belly
Time-to-event data example 5 for non-proportional hazards working groupex5_widening
Time-to-event data example 6 for non-proportional hazards working groupex6_crossing
Fleming-Harrington weighting functionfh
Piecewise exponential survival estimationfit_pwexp
Derive analysis date for interim/final analysis given multiple conditionsget_analysis_date
Get date at which an event count is reachedget_cut_date_by_event
MaxCombo testmaxcombo
Magirr and Burman weighting functionmb
Simulated survival dataset with delayed treatment effectmb_delayed_effect
Milestone test for two survival curvesmilestone
Perform multiple tests on trial data cuttingmultitest
Permuted fixed block randomizationrandomize_by_fixed_block
RMST difference of 2 armsrmst
The piecewise exponential distributionrpwexp
Generate piecewise exponential enrollmentrpwexp_enroll
Simulation of fixed sample size design for time-to-event endpointsim_fixed_n
Simulate group sequential designs with fixed sample sizesim_gs_n
Simulate a stratified time-to-event outcome randomized trialsim_pw_surv
Summary of group sequential simulations.summary.simtrial_gs_wlr
Convert enrollment and failure rates from 'sim_fixed_n()' to 'sim_pw_surv()' formatto_sim_pw_surv
Weighted logrank testwlr wlr.counting_process wlr.tte_data